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Found 17 papers, 8 papers with code
Discrete distributions are learnable from metastable samples
This property allows us to learn the true model using a conditional likelihood based estimator, even when the samples come from a metastable distribution concentrated in a small region of the state space.
Forced oscillation source localization from generator measurements
Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations.
Learning Energy-Based Representations of Quantum Many-Body States
An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest.
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs sampling performance.
Combinatorial Optimization
Vocal Bursts Intensity Prediction
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
Overall, the proposed QASA protocol provides a useful tool for assessing the performance of current and emerging quantum annealing devices.
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.
Learning Continuous Exponential Families Beyond Gaussian
We address the problem of learning of continuous exponential family distributions with unbounded support.
Programmable Quantum Annealers as Noisy Gibbs Samplers
Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning.
Learning of Discrete Graphical Models with Neural Networks
In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model.
Real-time Anomaly Detection and Classification in Streaming PMU Data
Ensuring secure and reliable operations of the power grid is a primary concern of system operators.
Efficient Learning of Discrete Graphical Models
We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models.
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers.
Emerging Technologies Quantum Physics
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations.
Information Theoretic Optimal Learning of Gaussian Graphical Models
What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered?
Optimal structure and parameter learning of Ising models
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning.
Graphical Models for Optimal Power Flow
In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors.
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.
